{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: ExperimentManager package not loaded (https://github.com/lucfra/ExperimentManager)\n",
      "No module named 'experiment_manager'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NOT ALL DATASETS AVAILABLE\n",
      "PLEASE INSTALL experiment_manager package\n",
      "get it at https://github.com/lucfra/ExperimentManager\n",
      "models.py; WARNING: some functions may not work\n",
      "No module named 'experiment_manager'\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import os\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import tensorflow.contrib.layers as layers\n",
    "import far_ho as far\n",
    "import far_ho.examples as far_ex\n",
    "\n",
    "tf.logging.set_verbosity(tf.logging.ERROR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from IPython.display import clear_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    sess.close()\n",
    "except: pass\n",
    "rnd = np.random.RandomState(1)\n",
    "tf.reset_default_graph()\n",
    "sess = tf.InteractiveSession()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Far-HO Demo, AutoML 2018, ICML workshop\n",
    "### Far-HO: a Bilevel Programming Package for Hyperparameter Optimization and meta-learning \n",
    "\n",
    "[Link to the workshop paper](https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxhdXRvbWwyMDE4aWNtbHxneDoyN2I3YmIxNTkyMjMwYjRm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "def to_one_hot_enc(seq, dimension=None):\n",
    "    da_max = dimension or int(np.max(seq)) + 1\n",
    "    _tmp = np.zeros((len(seq), da_max))\n",
    "    _tmp[range(len(_tmp)), np.array(seq, dtype=int)] = 1\n",
    "    return _tmp\n",
    "\n",
    "def flip(targets, p):\n",
    "    tgs = np.argmax(targets, axis=1)\n",
    "    nfl = int(len(targets)*p)\n",
    "    tgs[:nfl] = (tgs[:nfl] + rnd.randint(1, 9, size=(nfl,))) % 10\n",
    "    return to_one_hot_enc(tgs)\n",
    "\n",
    "def get_data(p, small=True):\n",
    "    # load a small portion of mnist data\n",
    "    if small: part = (.02, .002,)\n",
    "    else: part = (.5, .1)\n",
    "    datasets = far_ex.mnist(data_root_folder=os.path.join(os.getcwd(), 'MNIST_DATA'), partitions=part)\n",
    "    if p: \n",
    "        datasets.train._target = flip(datasets.train.target, p)\n",
    "    return datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "def g_logits(x,y, name='model'):\n",
    "    with tf.variable_scope(name):\n",
    "        h1 = layers.fully_connected(x, 300)\n",
    "        logits = layers.fully_connected(h1, int(y.shape[1]))\n",
    "    return logits\n",
    "\n",
    "def g_logits_d(x,y, name='model'):\n",
    "    return far_ex.fixed_init_ffnn(x, dims=(x.shape[1], 400, 400, 400, y.shape[1]), name=name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find automatically the weights of each training example  (DATA HYPER-CLEANER)\n",
    "#### with reverse-mode hypergradient  \n",
    "NOISE ON LABELS of the examples  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting /home/franceschi/TmpProj/FAR-HO/far_ho/examples/autoMLDemos/MNIST_DATA/mnist/train-images-idx3-ubyte.gz\n",
      "Extracting /home/franceschi/TmpProj/FAR-HO/far_ho/examples/autoMLDemos/MNIST_DATA/mnist/train-labels-idx1-ubyte.gz\n",
      "Extracting /home/franceschi/TmpProj/FAR-HO/far_ho/examples/autoMLDemos/MNIST_DATA/mnist/t10k-images-idx3-ubyte.gz\n",
      "Extracting /home/franceschi/TmpProj/FAR-HO/far_ho/examples/autoMLDemos/MNIST_DATA/mnist/t10k-labels-idx1-ubyte.gz\n",
      "datasets.redivide_data:, computed partitions numbers - [0, 1400, 1540, 70000] len all 70000 DONE\n"
     ]
    }
   ],
   "source": [
    "x = tf.placeholder(tf.float32, shape=(None, 28**2), name='x')\n",
    "y = tf.placeholder(tf.float32, shape=(None, 10), name='y')\n",
    "logits = g_logits(x, y)\n",
    "P = 1/3\n",
    "train_set, validation_set, test_set = get_data(p=P)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "lambdas = far.get_hyperparameter('lambdas', tf.zeros(train_set.num_examples))\n",
    "lr = far.get_hyperparameter('lr', initializer=0.02, constraint=lambda t: tf.maximum(tf.minimum(t, 1.), 1.e-5))\n",
    "\n",
    "ce = tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=logits)\n",
    "example_weights = tf.sigmoid(lambdas)\n",
    "L = tf.reduce_mean(example_weights*ce)\n",
    "E = tf.reduce_mean(ce)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "inner_optimizer = far.GradientDescentOptimizer(lr)\n",
    "outer_optimizer = tf.train.AdamOptimizer(0.02)\n",
    "hyper_step = far.HyperOptimizer().minimize(E, outer_optimizer, L, inner_optimizer)\n",
    "\n",
    "T = 100  # Number of inner iterations\n",
    "train_set_supplier = train_set.create_supplier(x, y)\n",
    "validation_set_supplier = validation_set.create_supplier(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "tf.global_variables_initializer().run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "def show_img(ax, data, label, other_info=\"\"):\n",
    "    ax.imshow(np.reshape(data, (28, 28)), cmap='gray')\n",
    "    ax.grid()\n",
    "    ax.set_title('label {} {:.3}'.format(np.argmax(label), other_info))\n",
    "    ax.tick_params(axis='both', which='both', bottom=False, top=False, labelbottom=False)\n",
    "\n",
    "def make_plots1():\n",
    "    clear_output()\n",
    "    print('inner:', L.eval(train_set_supplier()))\n",
    "    print('outer:', E.eval(validation_set_supplier()))\n",
    "    print('learning rate', lr.eval())\n",
    "    print('norm of examples weights', tf.norm(lambdas).eval())\n",
    "    print('-'*50)\n",
    "\n",
    "    fig, ax = plt.subplots(ncols=5, figsize=(16, 3))\n",
    "\n",
    "    img_ax = 0\n",
    "    for img_idx in range(4):\n",
    "        _id = rnd.randint(0, 1400)\n",
    "        show_img(ax[img_ax], train_set.data[_id], train_set.target[_id], example_weights.eval()[_id])\n",
    "        img_ax+=1\n",
    "#         plt.show()\n",
    "    evv = example_weights.eval()\n",
    "    \n",
    "    plt.sca(ax[-1])\n",
    "    xx = [0, 1]\n",
    "    flipped =  evv[:int(P*len(evv))]\n",
    "    clean = evv[int(P*len(evv)):]\n",
    "    ax[-1].set_title('Weights of examples')\n",
    "    plt.xticks([0, 1], ['Flipped', 'Clean'])\n",
    "    ax[-1].errorbar([0, 1], [np.mean(flipped), np.mean(clean)], yerr=[np.std(flipped), np.std(clean)], fmt='o')\n",
    "    ax[-1].set_xlim((-0.5, 1.5))\n",
    "    ax[-1].yaxis.tick_right()\n",
    "\n",
    "\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3YAAADSCAYAAAAGyFLoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nes5gJ0mSJEk99/8BgIGAqRwkNpUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 3))\n",
    "plt.bar(list(range(train_set.num_examples)), example_weights.eval(), width=1.);\n",
    "plt.xlim(0,1400)\n",
    "plt.title('Weight of the examples at the beginning');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner: 0.71312135\n",
      "outer: 0.97641593\n",
      "learning rate 0.17004915\n",
      "norm of examples weights 5.122146\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7wAAADSCAYAAABpVcyAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRCSV/j+f6WOO1QJzfwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x216 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# examples \n",
    "n_hyper_iterations = 10\n",
    "for hyt in range(n_hyper_iterations):\n",
    "    hyper_step(T,\n",
    "               inner_objective_feed_dicts=train_set_supplier,\n",
    "               outer_objective_feed_dicts=validation_set_supplier)\n",
    "    if hyt % 2 ==0:\n",
    "        make_plots1()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1400)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3YAAADCCAYAAAAFHlBzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 3))\n",
    "plt.bar(list(range(train_set.num_examples)), example_weights.eval(), width=1.);\n",
    "plt.xlim(0,1400)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimize few hyperparameters  with `ForwardHG`\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    sess.close()\n",
    "except: pass\n",
    "tf.reset_default_graph()\n",
    "sess = tf.InteractiveSession()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting /home/franceschi/TmpProj/FAR-HO/far_ho/examples/autoMLDemos/MNIST_DATA/mnist/train-images-idx3-ubyte.gz\n",
      "Extracting /home/franceschi/TmpProj/FAR-HO/far_ho/examples/autoMLDemos/MNIST_DATA/mnist/train-labels-idx1-ubyte.gz\n",
      "Extracting /home/franceschi/TmpProj/FAR-HO/far_ho/examples/autoMLDemos/MNIST_DATA/mnist/t10k-images-idx3-ubyte.gz\n",
      "Extracting /home/franceschi/TmpProj/FAR-HO/far_ho/examples/autoMLDemos/MNIST_DATA/mnist/t10k-labels-idx1-ubyte.gz\n",
      "datasets.redivide_data:, computed partitions numbers - [0, 35000, 42000, 70000] len all 70000 DONE\n"
     ]
    }
   ],
   "source": [
    "x = tf.placeholder(tf.float32, shape=(None, 28**2), name='x')\n",
    "y = tf.placeholder(tf.float32, shape=(None, 10), name='y')\n",
    "logits = g_logits_d(x, y, name='mod3')\n",
    "P = None\n",
    "train_set, validation_set, test_set = get_data(p=P, small=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# lambdas = far.get_hyperparameter('lambdas', tf.zeros(train_set.num_examples))\n",
    "lr = far.get_hyperparameter('lr', initializer=0.02, constraint=lambda t: tf.maximum(tf.minimum(t, .1), 1.e-5))\n",
    "mu = far.get_hyperparameter('mu', initializer=0.9, constraint=lambda t: tf.maximum(tf.minimum(t, .99), 1.e-5))\n",
    "rho = far.get_hyperparameter('rho', initializer=0.00001, constraint=lambda t: tf.maximum(tf.minimum(t, 0.01), 0.))\n",
    "\n",
    "\n",
    "ce = tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=logits)\n",
    "L = tf.reduce_mean(ce) + rho*tf.add_n([tf.reduce_sum(w**2) for w in tf.trainable_variables()])\n",
    "E = tf.reduce_mean(ce)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "inner_optimizer = far.MomentumOptimizer(lr, mu)\n",
    "outer_optimizer = tf.train.AdamOptimizer(1.e-3, beta1=0.5)\n",
    "hyper_step = far.HyperOptimizer(far.ForwardHG()).minimize(E, outer_optimizer, L, inner_optimizer)\n",
    "\n",
    "train_set_supplier = train_set.create_supplier(x, y, batch_size=256)  # stochastic GD\n",
    "validation_set_supplier = validation_set.create_supplier(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_plots2(his):\n",
    "    clear_output()\n",
    "    print('inner:', L.eval(train_set_supplier()))\n",
    "    print('outer:', E.eval(validation_set_supplier()))\n",
    "    print('learning rate', lr.eval(), 'momentum', mu.eval(), 'l2 coefficient', rho.eval())\n",
    "    print('-'*50)\n",
    "    \n",
    "    fig, ax = plt.subplots(ncols=4, figsize=(15, 3))\n",
    "    ax[0].set_title('Learning rate')\n",
    "    ax[0].plot([e[0] for e in his])\n",
    "    \n",
    "    ax[1].set_title('Momentum factor')\n",
    "    ax[1].plot([e[1] for e in his]) \n",
    "    \n",
    "    ax[2].set_title('L2 regulariz.')\n",
    "    ax[2].plot([e[2] for e in his])\n",
    "    \n",
    "    ax[3].set_title('Tr. and val. errors')\n",
    "    ax[3].plot([e[3] for e in his])\n",
    "    ax[3].plot([e[4] for e in his])\n",
    "    ax[3].set_ylim((0.1, 0.28))\n",
    "    \n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "T = 200  # number of iterations (note that if T is too small then the L2 regularizer may not kick in..)\n",
    "his_params = []\n",
    "n_hyper_iterations = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf.global_variables_initializer().run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner: 0.15132636\n",
      "outer: 0.2135272\n",
      "learning rate 0.030248703 momentum 0.9101652 l2 coefficient 0.0\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3YAAADSCAYAAAAGyFLoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIhIyP0fWvKeXF1KaKoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_hyper_iterations = 20\n",
    "for hyt in range(n_hyper_iterations):\n",
    "    hyper_step(T,\n",
    "               inner_objective_feed_dicts=train_set_supplier,\n",
    "               outer_objective_feed_dicts=validation_set_supplier)\n",
    "    res = sess.run(far.hyperparameters()) + [L.eval(train_set_supplier()), \n",
    "                                             E.eval(validation_set_supplier())]\n",
    "    his_params.append(res)\n",
    "    make_plots2(his_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Too slow? Update the hyperparameters online!\n",
    "### [RTHO](http://proceedings.mlr.press/v70/franceschi17a): REAL-TIME HYPERPARAMETER OPTIMIZATION "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hyper_batch_size = 100  # Number of inner iterations\n",
    "T = 5000\n",
    "his_params = []\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf.global_variables_initializer().run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner: 0.39552397\n",
      "outer: 0.14832164\n",
      "learning rate 0.032777533 momentum 0.90378654 l2 coefficient 0.003659289\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3sAAADSCAYAAADzNtlYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYM8wDMMwDMMwDCMP+X8w9ubb7BXsPQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for hyt in range(T//hyper_batch_size):\n",
    "    hyper_step(hyper_batch_size,\n",
    "               inner_objective_feed_dicts=train_set_supplier,\n",
    "               outer_objective_feed_dicts=validation_set_supplier, online=True)\n",
    "    res = sess.run(far.hyperparameters()) + [L.eval(train_set_supplier()), \n",
    "                                             E.eval(validation_set_supplier())]\n",
    "    his_params.append(res)\n",
    "    if hyt % 2 ==0:\n",
    "        make_plots2(his_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finally there is a third strategy, in between the two which uses warm-restart but discards past information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "T = 200  # number of iterations (note that if T is too small then the L2 regularizer may not kick in..)\n",
    "his_params = []\n",
    "n_hyper_iterations = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf.global_variables_initializer().run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner: 0.059396647\n",
      "outer: 0.10398966\n",
      "learning rate 0.024183922 momentum 0.8898875 l2 coefficient 0.00016986189\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3sAAADSCAYAAADzNtlYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nitiq4b6biQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for hyt in range(n_hyper_iterations):\n",
    "    \n",
    "    hyper_step(hyper_batch_size,\n",
    "               inner_objective_feed_dicts=train_set_supplier,\n",
    "               outer_objective_feed_dicts=validation_set_supplier, online='wr')  # wr stands for warm restart \n",
    "    res = sess.run(far.hyperparameters()) + [L.eval(train_set_supplier()), \n",
    "                                             E.eval(validation_set_supplier())]\n",
    "    his_params.append(res)\n",
    "    if hyt % 2 ==0:\n",
    "        make_plots2(his_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
